## Running select filename from dbo.sysfiles returns short foldernames with tilde

I am running the following statement in SQL Server 2014(in Windows 2012R2):

select filename from dbo.sysfiles where fileid = 1

However it returns:

c:\progra~1\micros~1\mssql1~1.sql\mssql\data\master~1.mdf

I wish for the full path without the tilde.

(e.g C:\Program Files\Microsoft SQL Server\MSSQL12.SQL2014\MSSQL\DATA\master.mdf)

I tried upgrading the SQL Server 2014 Express to 2016 Express but it made no difference.

Any ideas? Thanks. Dave

## I can’t bind the  (tilde) key as hotkey for DoTA 2 scoreboard?

I’m using English (US,alt,intl) and for whatever reason the game doesn’t recognize when I use the  key. Any other keys work fine. Of course, changing my keyboard input to another language made the button where my ` is be something else and it works but it’s annoying to switch languages. Any ideas how to use tilde in DoTA 2?

## If the Hausforff dimension of the graph of a function $u$ is $N$ and $\tilde u = u$ a.e. then $\dim_H \mathrm{graph} \, \tilde u = N$ too

Let $$u:\Omega \subset \mathbb{R}^N \to \mathbb{R}^M$$ be a function such that the Hausdorff dimension of its graph is $$N$$.

Let $$\tilde u = u$$ a.e. Is it true that the Hausdorff dimension of the graph of $$\tilde u$$ is also $$N$$?

## In the terminal tilde sign keeps printing as soon as I launch the terminal

In the terminal tilde sign keeps printing. If I press the tilde button in my keyboard it stop but it starts as soon as I press the enter button. Anyone faced this issue. I am attaching a screenshot for the same. I am typing this question from the same keyboard/laptop.

## Find a modified coupling $((X_n,\tilde Y_n))_{n∈ℕ_0}$ with the same coupling time $τ$ and $\tilde Y_n=X_n$ for $n≥τ$ in the coupling lemma

Let

• $$(\Omega,\mathcal A,\operatorname P)$$ be a probability space
• $$(E,\mathcal E)$$ be a measurable space
• $$(X_n)_{n\in\mathbb N_0}$$ and $$(Y_n)_{n\in\mathbb N_0}$$ be $$(E,\mathcal E)$$-valued time-homogeneous Markov chains on $$(\Omega,\mathcal A,\operatorname P)$$ with common transition kernel $$\kappa$$ and $$Z_n:=(X_n,Y_n)\;\;\;\text{for }n\in\mathbb N_0$$
• $$\mathcal F^X$$, $$\mathcal F^Y$$ and $$\mathcal F^Z$$ denote the filtraiton generated by $$X$$, $$Y$$ and $$Z$$, respectively

It’s easy to see that $$\tau:=\inf\left\{n\in\mathbb N_0:X_n=Y_n\right\}$$ is an $$\mathcal F^Z$$-stopping time and hence $$\tilde Y_n:=1_{\left\{\:n\:<\:\tau\:\right\}}Y_n+1_{\left\{\:n\:\ge\:\tau\:\right\}}X_n\;\;\;\text{for }n\in\mathbb N_0$$ is $$\mathcal F^Z$$-adapted. Moreover, $$\mathcal F^Z=\mathcal F^X\vee\mathcal F^Y$$.

How can we show that $$\tilde Y$$ is a time-homogeneous Markov chain with the same distribution as $$Y$$?

I guess the basic idea is that $$Z$$ is clearly a time-homogeneous Markov chain with transition kernel $$\pi$$ satisfying $$\pi((x,y),B_1\times B_2)=\kappa(x,B_1)\kappa(y,B_2)\;\;\;\text{for all }x,y\in E\text{ and }B_i\in\mathcal E\tag1.$$ Since $$\mathbb N_0$$ is countable, $$Z$$ is strongly Markovian at $$\tau$$ and hence $$1_{\left\{\:\tau\:<\:\infty\:\right\}}\operatorname E\left[f\left(\left(Z_{\tau+n}\right)_{n\in\mathbb N_0}\right)\mid\mathcal F_\tau\right]=1_{\left\{\:\tau\:<\:\infty\:\right\}}(\pi f)(Z_\tau)\;\;\;\text{almost surely}\tag2,$$ where $$\pi f:=\int\pi(\;\cdot\;,{\rm d}z)f(z)$$, for all bounded and $$(\mathcal E\otimes\mathcal E)^{\otimes\mathbb N_0}$$-measurable $$f:(E\times E)^{\mathbb N_0}\to\mathbb R$$. So, if $$k\in\mathbb N_0$$, $$n_0,\ldots,n_k\in\mathbb N_0$$ with $$0=n_0<\cdots and $$B\in\mathcal E^{\otimes k}$$, we obtain $$$$\begin{split}1_{\left\{\:\tau\:<\:\infty\:\right\}}\operatorname P\left[\left(\tilde Y_{\tau+n_1},\ldots,\tilde Y_{\tau+n_k}\right)\in B\mid\mathcal F_\tau\right]&=1_{\left\{\:\tau\:<\:\infty\:\right\}}\bigotimes_{i=1}^k\kappa^{n_i-n_{i-1}}(X_\tau,B)\&=1_{\left\{\:\tau\:<\:\infty\:\right\}}\operatorname P\left[\left(Y_{\tau+n_1},\ldots,Y_{\tau+n_k}\right)\in B\mid\mathcal F_\tau\right]\end{split}\tag3$$$$ almost surely.

However, it’s neither clear to me how we can conclude that $$\tilde Y$$ is Markovian (with respect to its generated filtration) nor why it has the same distribution as $$Y$$.

Clearly, the distribution of $$Y$$ is uniquely determined by the finite-dimensional distributions $$\operatorname P\left[\left(Y_{n_1},\ldots,Y_{n_k}\right)\;\cdot\;\right]$$ (and the same applies to $$\tilde Y$$). Moreover, we may write $$\operatorname P\left[\left(\tilde Y_{n_1},\ldots,\tilde Y_{n_k}\right)\;\cdot\;\right]=\operatorname P\left[n<\tau,\left(Y_{n_1},\ldots,Y_{n_k}\right)\;\cdot\;\right]+\operatorname P\left[n\ge\tau,\left(X_{n_1},\ldots,X_{n_k}\right)\;\cdot\;\right]\tag4.$$ Many pieces, I’m not able to combine.

## Find a modified coupling $((X_n,\tilde Y_n))_{n∈ℕ_0}$ with the same coupling time $τ$ and $\tilde Y_n=X_n$ for $n≥τ$ in the coupling lemma

Let

• $$(\Omega,\mathcal A,\operatorname P)$$ be a probability space
• $$(E,\mathcal E)$$ be a measurable space
• $$(X_n)_{n\in\mathbb N_0}$$ and $$(Y_n)_{n\in\mathbb N_0}$$ be $$(E,\mathcal E)$$-valued time-homogeneous Markov chains on $$(\Omega,\mathcal A,\operatorname P)$$ with common transition kernel $$\kappa$$ and $$Z_n:=(X_n,Y_n)\;\;\;\text{for }n\in\mathbb N_0$$
• $$\mathcal F^X$$, $$\mathcal F^Y$$ and $$\mathcal F^Z$$ denote the filtraiton generated by $$X$$, $$Y$$ and $$Z$$, respectively

It’s easy to see that $$\tau:=\inf\left\{n\in\mathbb N_0:X_n=Y_n\right\}$$ is an $$\mathcal F^Z$$-stopping time and hence $$\tilde Y_n:=1_{\left\{\:n\:<\:\tau\:\right\}}Y_n+1_{\left\{\:n\:\ge\:\tau\:\right\}}X_n\;\;\;\text{for }n\in\mathbb N_0$$ is $$\mathcal F^Z$$-adapted. Moreover, $$\mathcal F^Z=\mathcal F^X\vee\mathcal F^Y$$.

How can we show that $$\tilde Y$$ is a time-homogeneous Markov chain with the same distribution as $$Y$$?

I guess the basic idea is that $$Z$$ is clearly a time-homogeneous Markov chain with transition kernel $$\pi$$ satisfying $$\pi((x,y),B_1\times B_2)=\kappa(x,B_1)\kappa(y,B_2)\;\;\;\text{for all }x,y\in E\text{ and }B_i\in\mathcal E\tag1.$$ Since $$\mathbb N_0$$ is countable, $$Z$$ is strongly Markovian at $$\tau$$ and hence $$1_{\left\{\:\tau\:<\:\infty\:\right\}}\operatorname E\left[f\left(\left(Z_{\tau+n}\right)_{n\in\mathbb N_0}\right)\mid\mathcal F_\tau\right]=1_{\left\{\:\tau\:<\:\infty\:\right\}}(\pi f)(Z_\tau)\;\;\;\text{almost surely}\tag2,$$ where $$\pi f:=\int\pi(\;\cdot\;,{\rm d}z)f(z)$$, for all bounded and $$(\mathcal E\otimes\mathcal E)^{\otimes\mathbb N_0}$$-measurable $$f:(E\times E)^{\mathbb N_0}\to\mathbb R$$. So, if $$k\in\mathbb N_0$$, $$n_0,\ldots,n_k\in\mathbb N_0$$ with $$0=n_0<\cdots and $$B\in\mathcal E^{\otimes k}$$, we obtain $$$$\begin{split}1_{\left\{\:\tau\:<\:\infty\:\right\}}\operatorname P\left[\left(\tilde Y_{\tau+n_1},\ldots,\tilde Y_{\tau+n_k}\right)\in B\mid\mathcal F_\tau\right]&=1_{\left\{\:\tau\:<\:\infty\:\right\}}\bigotimes_{i=1}^k\kappa^{n_i-n_{i-1}}(X_\tau,B)\&=1_{\left\{\:\tau\:<\:\infty\:\right\}}\operatorname P\left[\left(Y_{\tau+n_1},\ldots,Y_{\tau+n_k}\right)\in B\mid\mathcal F_\tau\right]\end{split}\tag3$$$$ almost surely.

However, it’s neither clear to me how we can conclude that $$\tilde Y$$ is Markovian (with respect to its generated filtration) nor why it has the same distribution as $$Y$$.

Clearly, the distribution of $$Y$$ is uniquely determined by the finite-dimensional distributions $$\operatorname P\left[\left(Y_{n_1},\ldots,Y_{n_k}\right)\;\cdot\;\right]$$ (and the same applies to $$\tilde Y$$). Moreover, we may write $$\operatorname P\left[\left(\tilde Y_{n_1},\ldots,\tilde Y_{n_k}\right)\;\cdot\;\right]=\operatorname P\left[n<\tau,\left(Y_{n_1},\ldots,Y_{n_k}\right)\;\cdot\;\right]+\operatorname P\left[n\ge\tau,\left(X_{n_1},\ldots,X_{n_k}\right)\;\cdot\;\right]\tag4.$$ Many pieces, I’m not able to combine.